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Record W2139958433 · doi:10.1109/iros.1995.525873

The virtual handle [force-reflecting mechanisms]

2002· article· en· W2139958433 on OpenAlexaff
R. Hui, P. Gregorio

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsHaptic technologyDegrees of freedom (physics and chemistry)Computer scienceMechanism (biology)Operator (biology)Virtual realitySimulationControl theory (sociology)Control (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Many force-reflecting mechanisms with two to six degrees of freedom have been designed in the past for master-slave and haptic applications. Both serial and parallel mechanisms increase in mechanical, modeling and control complexity at a greater-than-linear rate with additional degrees of freedom. On the other hand, using two or three reduced-dof mechanisms only multiplies the complexity of each individual system by the number of mechanisms. The concept of the virtual handle is based on using two or three mechanisms each with three or four dofs, with a digit of the operator's hand operating one each. The mechanisms can be controlled in such a way that the operator feels as if a virtual device with a greater motion freedom than each individual mechanism is being grasped. The virtual handle can be made to emulate the operator's tool of choice, including a gripper, with the necessary number of virtual pushbuttons.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0200.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.219
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2002
Admission routes1
Has abstractyes

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